PyTorch [1]} framework is utilized to estimate the impact on accuracy for our gradient pruning method. The straight-through estimator (STE) is adopted in our implementation. We introduce an extra Pruning layer for different Conv block as shown in Fig. REF . As mentioned above, the input and output of this layer can be denoted as \(\mathbf {I}\)  and \(\mathbf {O}\) . The essence of this Pruning layer is a STE which can be defined as below:
\(\textbf {Forward: } {\mathbf {O} = \mathbf {I}}\) 
\(\textbf {Backward: }{\mathrm {d}\mathbf {I} = \textit {Stochastic\_Pruning} \left( \mathrm {d}\mathbf {O}, \textit {DBTD}(\mathrm {d}\mathbf {O}, p) \right)}\) 
